



'''simple docstring'''





import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class __A     (    unittest.TestCase		):



 '''simple docstring'''







 @property
 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      torch.manual_seed(0			)
      _a           =    UNetaDModel(
          block_out_channels=(32, 64)					,  layers_per_block=2					,  sample_size=32					,  in_channels=3					,  out_channels=3					,  down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')					,  up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')					,  )
      return model



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    self.dummy_uncond_unet
      _a           =    KarrasVeScheduler()

      _a           =    KarrasVePipeline(unet=A					,  scheduler=A			)
      pipe.to(A			)
      pipe.set_progress_bar_config(disable=A			)

      _a           =    torch.manual_seed(0			)
      _a           =    pipe(num_inference_steps=2					,  generator=A					,  output_type='''numpy'''			).images

      _a           =    torch.manual_seed(0			)
      _a           =    pipe(num_inference_steps=2					,  generator=A					,  output_type='''numpy'''					,  return_dict=A			)[0]

      _a           =    image[0, -3:, -3:, -1]
      _a           =    image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _a           =    np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]			)

      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2






@slow
@require_torch
class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    '''google/ncsnpp-celebahq-256'''
      _a           =    UNetaDModel.from_pretrained(A			)
      _a           =    KarrasVeScheduler()

      _a           =    KarrasVePipeline(unet=A					,  scheduler=A			)
      pipe.to(A			)
      pipe.set_progress_bar_config(disable=A			)

      _a           =    torch.manual_seed(0			)
      _a           =    pipe(num_inference_steps=20					,  generator=A					,  output_type='''numpy'''			).images

      _a           =    image[0, -3:, -3:, -1]
      assert image.shape == (1, 256, 256, 3)
      _a           =    np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]			)

      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2











'''simple docstring'''







def       __UpperCamelCase					(      lowercase_       :  int = 1_000_000   ):
       """simple docstring"""




       a_						     =			set(range(3   ,       lowercase_   ,       2   )   )
       primes.add(2   )
       for p in range(3   ,       lowercase_   ,       2   ):
              if p not in primes:
                     continue
              primes.difference_update(set(range(p * p   ,       lowercase_   ,       lowercase_   )   )   )

       a_						     =			[float(lowercase_   ) for n in range(limit + 1   )]

       for p in primes:
              for n in range(lowercase_   ,       limit + 1   ,       lowercase_   ):
                     phi[n] *= 1 - 1 / p

       return int(sum(phi[2:]   )   )


if __name__ == "__main__":
  print(f"""{solution() = }""")




